CAEBM In A Nutshell

CAEBM: Some Buzzwords Touched

Computer-Aided Modeling

Parametric Modeling

AI-Based Modeling

Experience-Based Modeling

Example-Based Modeling

Machine Learning

Deep Learning

Big Data

Assumption-Free Modeling

Interdisciplinary Modeling

Holistic Modeling

Integration of Scientific, Rule-based, Example-based, and Heuristic Know-How

Accuracy-Controled Modeling

Nonlinear Statistics

High-Dimensional System Identification

Nonlinear Parameter Identification

Pattern Recognition

Time Series Prediction

Series Extrapolation

Data Driven Processes

Data Driven Business

Data Driven Anything

Data = Examples = Experience

CAEBM: Highlights

> Computer-Aided Example-Based Modeling (= CAEBM) can analyze and explain arbitrary, interdisciplinary, high-dimendinal problems from any domain,  employing models with as well numeric as nonnumeric parameters.

> The associative modeling is done based exclusively on examples, using "Genetic Neural Nets". No theories, no assumptions, no prejudices, no additional verifications are required: The problem-examples alone, collected from any source, generate the models, with built-in analysis of model complexity and accuracy.

> The resulting highly portable models can "explain" any relationship details between the participating parameters in 1 to 3 of many dimensions, making the models    understandable by and plausible for "1-3 dimensional" human users.

> As practically anything on earth can be seen as (a sequence of) examples, CAEBM can use the myriads of examples available, to detect the know-how contained, and to make it re-usable for humans and computers.

> In general, CAEBM can expand considerably the Experience-based problem understanding capabilities of humans, restricted normally to max 1-3 dimensions, the same way like Computer Aid (CA) has done it so impressively for Science-based problem understanding since several decades. A new fruitful balance between "Experience" and "Science" becomes possible.

CAEBM: Examples as Know-how Source

Know-how is the most important resource in today's businesses and organizations.

Know-how is distributed in people's heads, in science-based models, in test results, in rules, in example-collections etc.

Any know-how from any source, and anything (happening) in this world can be understood and represented by (a sequence of) examples, with numeric and nonnumeric parameters, if appropriate.


Our Example-Based Modeling (EBM) technology constructs high-dimensional parametric models, from examples only. Neural Nets are used as modeling kernel and Genetic Algorithms setup the models and train them.

We use Computer Aid (CA) for example preparation, iterative model setup, generalization & quality assurance, and parameter set optimization, resulting in our CAEBM technology: EBM+CA=CAEBM.

Our results are handy computer models, containing the example-based know-how from any source in arbitrary problem domains, defined by the parameter set in use, error measures included.

Our CAEBM technology can be used to collectconsolidatecontinuously refine, and systematically RE-USE any know-how in any problem domain.

Our CAEBM technology can construct  new solutions for high-dimendional, interdisciplinary, even scientifically unresolvable problems, to make them understandable at the same time for human brains, normally restricted to max 2-3 dim problem understanding.

Our CAEBM technology makes know-how becoming a portabletradable product, independent from the know-how sources. This allows for totally new business opportunities, eg concentrating on know-how collection and refinement in their special problem domains. And until now  "impossible" problem solutions become feasible.***

CAEBM: Some Application Patterns

*** Design of predictive local, regional, national etc  Covid-19 strategies, based on worldwide examples of infections and contra-measures taken, minimizing the damage done to people's health, to personal freedom, and to economy, while avoiding pandemic developments.

*** Design of a low-cost, self-learning, local, regional, national etc weather prediction network, based on the weather itself as example source, making short to long-time predictions as needed.

*** Design of a self-learning, inter-modal, local, regional, national etc traffic control network, based on the traffic itself as example source, minimizing traffic burden + maximizing transport performance for a given infrastructure, and identifying cost-minimal bottle-neck eliminations + most cost-efficient infrastructure developments.

*** Setup of "intelligent", self-learning test stands, which collect their experiences gained so far by CAEBM technology in test stand-specific models (TSSMs), ready then to do additional test jobs in a fraction of time, because most often new test jobs can be mostly fullfiled by the TSSMs, and only a few additional test stand runs are needed, to "calibrate" the experience to the new test job.

*** Setup and maintenance of a know-how network of CAEBMs for the development of a family of products (eg a family of cars), to be used for super-fast development (and production) of customer-individual products.

*** and many more...